'Institute of Electrical and Electronics Engineers (IEEE)'
Doi
Abstract
Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. E. Gonzalez-Sosa, R. Vera-Rodriguez, J. Fierrez, P. Tome and J. Ortega-Garcia, "Pose Variability Compensation Using Projective Transformation for Forensic Face Recognition," Biometrics Special Interest Group (BIOSIG), 2015 International Conference of the, Darmstadt, 2015, pp. 1-5. doi: 10.1109/BIOSIG.2015.7314615The forensic scenario is a very challenging problem within the face recognition community. The verification problem in this case typically implies the comparison between a high quality controlled image against a low quality image extracted from a close circuit television (CCTV). One of the downsides that frequently presents this scenario is pose deviation since CCTV devices are usually placed in ceilings and the subject normally walks facing forward. This paper proves the value of the projective transformation as a simple tool to compensate the pose distortion present in surveillance images in forensic scenarios. We evaluate the influence of this projective transformation over a baseline system based on principal component analysis and support vector machines (PCA-SVM) for the SCface database. The application of this technique improves greatly the performance, being this improvement more striking with closer images. Results suggest the convenience of this transformation within the preprocessing stage of all CCTV images. The average relative improvement reached with this method is around 30% of EER.This work has been partially supported in part by Bio-Shield (TEC2012-34881) from Spanish MINECO, in part by BEAT (FP7-SEC-284989) from EU and in part by Cátedra UAM-Telefónica. E. Gonzalez-Sosa is supported by a PhD scholarship from Universidad Autonoma de Madrid
